R Notebook

library(shiny)
library(tidyverse)
library(shinythemes)
library(stringi)

seabirds_cleaned_data <- read_csv("clean_data/seabirds_cleaned_data.csv")

Rows: 49020 Columns: 52
-- Column specification -------------------------------------------------------------------------------------------------------------------------------------------------------------
Delimiter: ","
chr  (19): common_name, scientific_name, species_abbreviation, age, plphase, feeding, on_water, on_ice, on_ship, in_hand, fly_by, group_sighting, ship_wake, molting, nat_feeding...
dbl  (28): record_x, record_id, wanplum, total_sighting, num_feeding, num_on_water, num_on_ice, num_fly_by, num_group_sighting, num_ship_wake, record_y, lat, long, ship_activity...
lgl   (3): sex, air_temp, salinity
dttm  (2): date, time

i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.

birds_21 <- seabirds_cleaned_data %>%
  mutate(bird_type = case_when(
  str_detect(common_name, 
             regex("shearwater", 
                   ignore_case = TRUE)) ~ "Shearwater",
  str_detect(common_name, 
             regex("albatross", 
                   ignore_case = TRUE)) ~ "Albatross",
  str_detect(common_name, 
             regex("mollymawk", 
                   ignore_case = TRUE)) ~ "Mollymawk",
  str_detect(common_name, 
             regex("petrel", 
                   ignore_case = TRUE)) ~ "Petrel",
  str_detect(common_name, 
             regex("prion", 
                   ignore_case = TRUE)) ~ "Prion",
  str_detect(common_name, 
             regex("skua", 
                   ignore_case = TRUE)) ~ "Skua",
  str_detect(common_name, 
             regex("penguin", 
                   ignore_case = TRUE)) ~ "Penguin",
  str_detect(common_name, 
             regex("tropicbird", 
                   ignore_case = TRUE)) ~ "Tropicbird",
  str_detect(common_name, 
             regex("noddy", 
                   ignore_case = TRUE)) ~ "Noddy",
  str_detect(common_name, 
             regex("tern", 
                   ignore_case = TRUE)) ~ "Tern",
  str_detect(common_name, 
             regex("gull", 
                   ignore_case = TRUE)) ~ "Gull",
  str_detect(common_name, 
             regex("booby", 
                   ignore_case = TRUE)) ~ "Booby",
  str_detect(common_name, 
             regex("frigatebird", 
                   ignore_case = TRUE)) ~ "Frigatebird",
  str_detect(common_name, 
             regex("shag", 
                   ignore_case = TRUE)) ~ "Shag",
  str_detect(common_name, 
             regex("sheathbill", 
                   ignore_case = TRUE)) ~ "Sheathbill",
  str_detect(common_name, 
             regex("fulmar", 
                   ignore_case = TRUE)) ~ "Fulmar",
  str_detect(common_name, 
             regex("gannet", 
                   ignore_case = TRUE)) ~ "Gannet",
  str_detect(common_name, 
             regex("cormorant", 
                   ignore_case = TRUE)) ~ "Cormorant",
  str_detect(common_name, 
             regex("procellaria", 
                   ignore_case = TRUE)) ~ "Procellaria",
    TRUE ~ common_name))

birds_21

# https://r-charts.com/color-palette-generator/

# https://www.statology.org/color-by-factor-ggplot2/

birds_pal <- c("#50e2ea", "#4edae5", "#4bd2df", "#49cada", "#47c2d4", 
               "#45bbcf", "#42b3c9", "#40abc4", "#3ea3be", "#3b9bb9",
               "#3993b3", "#378bae", "#3483a8", "#327ba3", "#30739d", 
               "#2e6c98", "#2b6492", "#295c8d", "#275487", "#244c82", "#22447c")

names(birds_pal) <- levels(birds_21$bird_type)

custom_colors <- scale_colour_manual(values = birds_pal)
  

birds <- c("Tropicbird" = "#50e2ea", "Tern" = "#4edae5", "Skua" = "#4bd2df", 
           "Sheathbill" = "#49cada", "Shearwater" = "#47c2d4", 
           "Shag" = "#45bbcf", "Seabird" = "#42b3c9", "Procellaria" = "#40abc4",
           "Prion" = "#3ea3be", "Petrel" = "#3b9bb9", "Penguin" = "#3993b3", 
           "Noddy" = "#378bae", "Mollymawk" = "#3483a8", "Jaeger" = "#327ba3", 
           "Gull" = "#30739d", "Gannet" = "#2e6c98", "Fulmar" = "#2b6492", 
           "Frigatebird" = "#295c8d", "Cormorant" = "#275487", 
           "Booby" = "#244c82", "Albatross" = "#22447c")

Jaeger Seabird

birds_9 %>% 
  filter(!is.na(bird_type)) %>% 
  count(bird_type)  

NA

sighting <-  birds_21 %>% 
            filter(!is.na(bird_type)) %>% 
            group_by(bird_type) %>% 
            summarise(count = sum(total_sighting, na.rm = TRUE)) %>%
            mutate(sighting_id = row_number())

sighting %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(breaks = c(1, 5, 10, 1000, 6000, 1400000),
                       limits = c(1,1400000), 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen \n Log10 scale") +
    scale_fill_manual(values = birds)

# log10() as 1 or more birds are less than 10 and don't show on normal graph

1,394,468

feeding <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(feeding, "YES")) %>% 
              summarise(count = n()) %>% 
              mutate(feeding_id = row_number())

feeding %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(breaks = c(1, 5, 10, 100, 300, 800),
                       limits = c(1,800), 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Feeding \n Log10 scale") +
    scale_fill_manual(values = birds)

# log10() as 1 or more birds are less than 10 and don't show on normal graph

on_ship <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(on_ship, "YES")) %>% 
              summarise(count = n()) %>% 
              mutate(on_ship_id = row_number())

on_ship %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(breaks = c(1, 2, 3, 5, 7, 10, 60),
                       limits = c(1,60), 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen On Ship") +
    scale_fill_manual(values = birds)

in_hand <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(in_hand, "YES")) %>% 
              summarise(count = n()) %>% 
              mutate(in_hand_id = row_number()) 

in_hand %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen In Hand") +
    scale_fill_manual(values = birds)

fly_by <-  birds_21 %>% 
              group_by(bird_type) %>% 
              filter(str_detect(fly_by, "YES")) %>% 
              summarise(count = n()) %>% 
              mutate(fly_by_id = row_number())

fly_by %>% 
    ggplot() +
    aes(y = bird_type, 
        x = count, fill = bird_type) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous(breaks = c(1, 5, 10, 1000, 6000), 
                       limits = c(1,6000), 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds)

# log10() as 1 or more birds are less than 10 and don't show on normal graph

variants <- birds_21 %>% 
              filter(bird_type == "Albatross") %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous( 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds_pal)            

variants <- birds_21 %>% 
              filter(bird_type == "Booby") %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous( 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds_pal)  

variants <- birds_21 %>% 
              filter(bird_type == "Cormorant") %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous( 
                       trans = "log10") +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds_pal) 

variants <- birds_21 %>% 
              filter(bird_type == "Tropicbird") %>% 
              group_by(common_name) %>% 
              summarise(count = n())
  
variants %>% 
    ggplot() +
    aes(y = common_name, 
        x = count, fill = common_name) +
    geom_col(colour = "black") +
    theme(legend.position = "none") +
    scale_x_continuous() +
    labs(y = "\n Bird Names",
         x = "Number of Birds Seen Flying BY\n Log10 scale") +
    scale_fill_manual(values = birds_pal) 

```r
library(shiny)
library(tidyverse)
library(shinythemes)


seabirds_cleaned_data <- read_csv(\data/seabirds_cleaned_data.csv\)

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```r
```r
birds_9 <- seabirds_cleaned_data %>% 
  group_by(common_name) %>% 
  mutate(common_name = if_else(str_detect(common_name, 
                                          \(?i)shearwater\),\Shearwater\, 
                               common_name),
         common_name = if_else(str_detect(common_name, 
                                          \(?i)albatross\), \Albatross\,
                               common_name),
         common_name = if_else(str_detect(common_name, 
                                          \(?i)mollymawk\), \Mollymawk\,
                               common_name),
         common_name = if_else(str_detect(common_name, 
                                          \(?i)petrel\), \Petrel\,
                               common_name),
         common_name = if_else(str_detect(common_name, 
                                          \(?i)prion\), \Prion\,
                               common_name),
         common_name = if_else(str_detect(common_name, 
                                          \(?i)skua\), \Skua\,
                               common_name),
         common_name = if_else(str_detect(common_name, 
                                          \(?i)penguin\), \Penguin\,
                               common_name),
         common_name = if_else(str_detect(common_name, 
                                          \(?i)Red-tailed tropicbird\), 
                               \Red-tailed tropicbird\,
                               common_name),
         common_name = if_else(str_detect(common_name, 
                                          \(?i)Brown noddy\), \Brown noddy\,
                               common_name)
  ) %>% 
  filter(common_name %in% c(\Shearwater\, \Albatross\, 
                            \Mollymawk\, \Petrel\, 
                            \Prion\, \Skua\, 
                            \Penguin\, \Brown noddy\, 
                            \Red-tailed tropicbird\))

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```r
```r
pal <- c(\Shearwater\ = \grey\, \Albatross\ = \blue\, 
         \Mollymawk\ = \yellow\, \Petrel\ = \green\, 
         \Prion\ = \pink\, \Skua\ = \purple\, 
         \Penguin\ = \orange\, \Brown noddy\ = \brown\, 
         \Red-tailed tropicbird\ = \red\)

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```r
```r
names(birds_9)
head(birds_9)

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```r
```r

birds_9 %>% 
  group_by(common_name) %>% 
  mutate(feeding = if_else(feeding %in% \YES\, 1, 0),
         on_ship = if_else(on_ship %in% \YES\, 1, 0),
         in_hand = if_else(in_hand %in% \YES\, 1, 0),
         fly_by = if_else(fly_by %in% \YES\, 1, 0)) %>% 
  summarise(sighting_count = sum(total_sighting, na.rm = TRUE),
            feeding_count = sum(feeding, na.rm = TRUE),
            on_ship_count = sum(on_ship, na.rm = TRUE),
            in_hand_count = sum(in_hand, na.rm = TRUE),
            fly_by_count = sum(fly_by, na.rm = TRUE)) 

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```r
library(leaflet)

Warning: package ‘leaflet’ was built under R version 4.1.2

  leaflet(data = birds_21) %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(label = birds_9$common_name, clusterOptions = markerClusterOptions()) 

Assuming "long" and "lat" are longitude and latitude, respectively
Warning in validateCoords(lng, lat, funcName) :
  Data contains 32 rows with either missing or invalid lat/lon values and will be ignored

  
 # Print the map

-45.91667 165.4000

seabirds_cleaned_data

ship_data <- read_excel(here("raw_data/seabirds.xls"), 
                             sheet = "Ship data by record ID") %>% 
              clean_names()

position <- ship_data %>% 
  select(date, lat, long) %>%
  filter(!is.na(lat),
         !is.na(long)) %>% 
  group_by(date) %>% 
  summarise_if(is.numeric, mean)

position

ship_data %>% 
  select(date, lat, long) %>%
  filter(is.na(date))

tail(position)

leaflet(data = position) %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(label = position$date, clusterOptions = markerClusterOptions()) 

Assuming "long" and "lat" are longitude and latitude, respectively
Warning in validateCoords(lng, lat, funcName) :
  Data contains 5 rows with either missing or invalid lat/lon values and will be ignored

  
 # Print the map

https://kateto.net/network-visualization

https://stackoverflow.com/questions/38432788/how-do-i-visualise-multiple-routes-using-leaflet-in-r

# https://rstudio.github.io/leaflet/markers.html
# first 20 quakes
df.20 <- quakes[1:20,]

getColor <- function(quakes) {
  sapply(position$date, function(date) {
  if(date <= 1979-12-31) {
    "green"
  } else if(date <= 1989-12-31) {
    "orange"
  } else {
    "red"
  } })
}

icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'ion',
  markerColor = getColor(position)
)

leaflet(position) %>% addTiles() %>%
  addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(date))

getColor <- function(quakes) {
  sapply(position$date, function(date) {
  if(date %in% "^196") {
    "green"
  } else if(date  %in% "^197") {
    "orange"
  } else if(date  %in% "^198") {
    "blue"
  } else {
    "red"
  } })
}

icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'ion',
  markerColor = getColor(position)
)

leaflet(position) %>% addTiles() %>%
  addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(date))

getColor <- function(quakes) {
  sapply(position$date, function(date) {
  case_when(str_detect(date, 
             regex("^196", 
                   ignore_case = TRUE)) ~ "green",
            str_detect(date, 
             regex("^197", 
                   ignore_case = TRUE)) ~ "orange",
             str_detect(date, 
             regex("^198", 
                   ignore_case = TRUE)) ~ "blue",
             str_detect(date, 
             regex("^199", 
                   ignore_case = TRUE)) ~ "red"
    
  ) })
}

icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'black',
  library = 'ion',
  markerColor = getColor(position)
)

leaflet(position) %>% addTiles() %>%
  addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(date))

library(dplyr)
library(shiny)
library(leaflet)
library(readxl)
library(RColorBrewer)
library(maps)
library(leaflet.extras)
library(htmlwidgets)



data_dots = read_csv("test4.csv")

Rows: 8 Columns: 14
-- Column specification -------------------------------------------------------------------------------------------------------------------------------------------------------------
Delimiter: ","
chr (6): Name, ship_date, delivery_date, ShipmentID, Dcity, Origin
dbl (8): Dzip, Dlong, Dlat, Route, Seq, Ozip, Olong, Olat

i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.

ui <- bootstrapPage(
  tags$style(type = "text/css", "html, body {width:100%;height:100%}"),
  leafletOutput("map", width = "100%", height = "100%"),
  absolutePanel(top = 10, right = 10,

                dateRangeInput("dateRange", "Date Range Input", start =  min(data_dots$ship_date), end = max(data_dots$ship_date))


  )
)

Warning: Couldn't coerce the `end` argument to a date string with format yyyy-mm-dd

server <- function(input, output) {

  #n <- 60
  qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual', ]
  col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))


  myMap = leaflet("map") %>% 
    addTiles(group = "Base") %>%
    addProviderTiles(providers$CartoDB.Positron, group = "Grey") %>%
    addResetMapButton()


  rv <- reactiveValues(
    filteredData =data_dots,
    ids = unique(data_dots$Route)
  )

  observeEvent(input$dateRange, 
               {rv$filteredData = data_dots[as.Date(data_dots$ship_date) >= input$dateRange[1] & as.Date(data_dots$ship_date) <= input$dateRange[2],]

               rv$ids = unique(rv$filteredData$Route)
               }

  )



  # Initiate the map
  output$map <- renderLeaflet({

    for (i in rv$ids) {
      #print(i)
      myMap = myMap %>%
        addPolylines(
          data = subset(rv$filteredData, Route == i),
          weight = 3,
          color = sample(col_vector, 1),
          opacity = 0.8,
          smoothFactor = 1,
          lng = ~Dlong, 
          lat = ~Dlat,
          highlight = highlightOptions(
            weight = 5,
            color = "blue",
            bringToFront = TRUE
          ),
          label = ~ as.character(ShipmentID),
          popup = ~ as.character(ShipmentID),
          group = "test"
        )

    }
    myMap


  })


}
shinyApp(ui = ui, server = server)

Listening on http://127.0.0.1:4277
Warning: Error in charToDate: character string is not in a standard unambiguous format
  [No stack trace available]

NA

data_dots %>% 
  mutate(ship_date = as.Date(ship_date, "%y/%m/%d"),
         delivery_date = as.Date(delivery_date, "%y/%m/%d"))